Abstract
Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.
Highlights
COVID-19 has been reported as a global epidemic by the World Health Organization (WHO), and it travels out across globe. 8.8 million cases were registered globally on June 30, 2020
The solution will participate in the fight against COVID-19 in an innovative and the current manner by integrating Artificial intelligence (AI) and deep learning algorithms in the digital health district. ere have been enormous executions of AI along with the techniques of deep learning (DL), which can be rational during the initial detection and monitoring processes of COVID-19 through the analysis of extracted features of coughing, breathing, and speech using the recurrent neural network (RNN) for coughing, breathing, and speech
IoT, AI, and big data innovations that assist with the COVID-19 problem and the several problems stated in that effort include telemedicine’s for the prevention and management of disease, the prevention of outbreaks and the minimization or even stoppage of the transmission of the virus, the use of drones to monitor for isolation, and mask use
Summary
COVID-19 has been reported as a global epidemic by the World Health Organization (WHO), and it travels out across globe. 8.8 million cases were registered globally on June 30, 2020. IoT technology may affect the whole company range, since every device and entity in the digital Internet network might be identified as having huge benefits. Such benefits often include enhanced connection between services, devices, and systems that go beyond the machine status [8]. An IoT health service consists of several server-linked detectors It provides real-time environmental or user tracking. Is search uses the machine learning and deep learning methods to monitor everyday behavior in anticipation of COVID-19’ future cross-country accessibility utilizing the official open information source in real-time. The solution will participate in the fight against COVID-19 in an innovative and the current manner by integrating AI and deep learning algorithms in the digital health district. The solution will participate in the fight against COVID-19 in an innovative and the current manner by integrating AI and deep learning algorithms in the digital health district. ere have been enormous executions of AI along with the techniques of deep learning (DL), which can be rational during the initial detection and monitoring processes of COVID-19 through the analysis of extracted features of coughing, breathing, and speech using the recurrent neural network (RNN) for coughing, breathing, and speech
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